A machine learning pipeline for classification of cetacean echolocation clicks in large underwater acoustic datasets
Fig 3
A similarity score based on correlation in time and frequency is used to associate similar signals and distinguish dissimilar signals.
In this illustration, similarity between 100 detections is displayed as a symmetric similarity matrix (A). The similarity between detection X and detection Y is given by the color of grid cell (X,Y) on a scale between 0 (low similarity) and 1 (high similarity). Black squares along the diagonal represent comparisons of each detection to itself, and are ignored. The 90th detection (a delphinid echolocation click) indicated by the black arrow in (A) is compared to two other detections: The blue triangle denotes a highly-similar detection, while the red square denotes a dissimilar detection. In (B) the same dataset is visualized as a network in which similar detections are attracted to each other and dissimilar detections repelled. The black node represents the 90th detection. Waveforms (C), waveform envelopes (D) and spectra (E) are shown for the three detections, with the original detection in black, the similar detection in blue, and the dissimilar detection in red. Waveforms and waveform envelopes have been offset by a constant value for readability. Plots C-E indicate that the detections with high similarity scores are alike in the time and frequency domains, while detection with a low similarity score is quite different from the other two.